@inproceedings{91d98a26ec1f4e628ae57cb3ab23b72c,
title = "Patient-Specific Heartbeat Classification Based on I-Vector Adapted Deep Neural Networks",
abstract = "Automatic heartbeat classification from electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A main challenge in ECG classification is the variability of ECG signals across patients. This paper proposes a patient-specific heartbeat classifier to address the inter-patient variations in ECG signals. Inspired by the success of identity vectors (i-vectors) in speech and speaker recognition, we extracted one i-vector from five minutes of ECG data for each patient and applied it to adapt a patient-independent deep neural network (DNN) to a patient-specific DNN, namely i-vector adapted patient-specific DNN (iAP-DNN). Evaluations on the MIT-BIH arrhythmia database show that the iAP-DNN is able to classify raw ECG signals of the corresponding patient into normal heartbeats and different types of arrhythmias and that it outperforms existing patient-specific classifiers in terms of sensitivity-vs-specificity and Mathews correlation coefficients.",
keywords = "Arrhythmias, Deep neural networks, DNN adaptation, ECG classification, i-vectors",
author = "Xu, {Sean Shensheng} and Mak, {Man Wai} and Cheung, {Chi Chung}",
year = "2018",
month = dec,
day = "3",
doi = "10.1109/BIBM.2018.8621475",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "784--787",
editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",
booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
note = "2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",
}